Computer information systems (“systems”) include computers that communicate with each other over a network, such as the Internet, and computers that manage information. For example, a healthcare enterprise uses systems to store and manage clinical and non-clinical information for patients in their care.
Healthcare enterprises employ simulation techniques to estimate, plan, or forecast the impact that changes to past operations will have on resources to service patients in the future. Factors that may be influenced or anticipated for a simulation include, for example: healthcare plans of employers, anticipated or planned changes in the medical staff, negotiations with third party payers, outreach facilities and changes in referral patterns, changes in the product line or delivery mode offered by the hospital, such as new, expanded, or discontinued services, patient population changes, and legislation and regulations.
When expectations for change can be captured by a single factor the task of quantifying expectations is straightforward. However, when multiple factors are involved, the problem is more complex and not satisfied by present solutions.
Prior case-mix simulation systems limit a simulation to a single factor such as diagnosis related groups (DRG) or doctor.
Prior simulation approaches that incorporate multiple factors include the following.
1. Set up a combined variable and treat it as a single variable. For example, create the combination “zip code—DRG,” wherein “19312-142,” for example, refers to patients living in zip code 19312 with DRG 142.
2. Use an algorithm that uses a tree-structured logic routine to create a single new dimension from multiple factors related to patients of a healthcare organization. The single new dimension is an indicator of case type or, in the aggregate, case mix. In this way, multiple dimensions are reduced to one dimension.
3. Use a tree-structure template as a model such as Doctors within DRGs. In this approach, rules may be defined at the DRG level, but, on an exception basis, go down to the Doctor level within selected DRGs.
4. Provide a combination of detailed rules and general rules for which the detailed rules would pre-empt the more general rule.
The first, third, and fourth approaches are limited in their flexibility, clumsy in their implementation, cumbersome to use, and unmanageable beyond two or three drivers. The second approach is flexible, but has other limitations described below.
The first approach creates too many combinations, each of which needs to be addressed separately. For example, suppose a patient population in zip code 19312 is going up by 20%, and in zip code 19301 is going down by 10%, while the obstetrics practices are being discontinued. A simulation for such an expectation requires entry of an expected percentage change for each combination of zip code and DRG in a database.
The second approach is similar to the first in that it reduces multiple dimensions to a single dimension. Although, the second approach is more sophisticated than the other approaches, it has a number of shortcomings. It is highly complex and its implementation requires software code to be written. Once the classification scheme for the approach has been settled and coded, it is rigidly applied. Because of this rigidity, the approach is often used by government agencies with the intent of classifying patients across multiple facilities (e.g., Medicare). The approach does not lend itself to settings in which the simulation scenarios can differ in the factors that are considered relevant, and in which the relevant factors are likely to change over time. The factors used in the tree-structured logic routine need to be generic factors, such as diagnosis codes, age, gender, etc., and do not incorporate factors such as zip code, physicians, and payer mix, which are needed when case mix simulation are utilized for budgeting and planning.
The third approach is more efficient that the first, but it forces the imposition of a tree structure of factors at the outset that does not reflect the underlying reality of the changes. Beyond two factors the third approach becomes increasingly restrictive and even the previous example in the first approach does not fit well into the third approach.
The fourth approach involves either an implicit or explicit hierarchy, and could not effectively be applied to the examples like the DRG and zip code.
The prior approaches increase processing burdens, increase computing resources, and reduce computational speed of simulation. Accordingly, there is a need for a medical resource estimation and simulation system that overcomes these and other disadvantages of the prior systems.